I recently started my machine learning PhD and I have absolutely no idea what I'm doing I started my PhD a few months ago.

Seriously, I don't know how I got accepted, but here I am. I am not a computer scientist, but come from a different STEM background. My knowledge in machine learning in general is pretty limited (for now, trying to catch up) and publishing something seems so far out of reach that it's overwhelming. I don't even know where to start. I'm reading papers on semantic segmentation and similar topics (which is the direction my topic goes in), and while I understand most of them technically, I completely lack the intuition to ask good questions about them, let alone identify some gap in which I can dive into to publish something. I read them and think "And now what?"

I don't dare to talk with my supervisor about this. She will think I am completely stupid and not suitable for the position.

Can someone suggest a pathway for me to somehow get the ball rolling and systematically work towards successfully graduating with a PhD? I'm really willing to put in work, it's just that right now I have the feeling I'm stumbling around in the dark and it's not productive at all.
Talk to your supervisor.  Your supervisor accepted you, and therefore she must think that you had something to offer her research group.  Don't phrase it like you did here.  Instead, come to her with solutions and not problems.  For instance, you need to learn to ask good questions.  That's great.  So why don't you ask her to recommend a good dataset that is common to use so that you can start exploring different methods?

Ultimately, you are in a PhD program and one of the biggest things you need to learn is how to read the literature to discover the open problems.  This requires you to read far and wide.  A big thing that will probably help you is to try to replicate results. You aren't doing this for publications, but to better understand what was done and why, and what the limitations were.  Take a recent paper and try to recreate it.  If you can't, go to a less recent paper that that one cites and so on.  Build your way up.

For what it's worth, I think that a lot of PhD students come in with a general understanding of the field but not much of a deep understanding. As an anecdote, in the beginning of my PhD I'd find a paper that was written years before I started and think "Oh no! They've solved all of the problems I thought were important!" I'd rush to my supervisor to show him the brilliant research, and he'd show me the limitations and how they were being addressed.  It seemed to me at the time that these papers solved their problems, but by spending time speaking to him I realized that that was not true - perhaps the data set was particularly simple, perhaps the researchers didn't try to expand their results too far, perhaps the domain in which the algorithm worked was so restricted that it was of no practical use.

Eventually, I learned how to do that on my own, first by looking for common obvious problems, then by thinking more subtly as I got better - and as I started doing my own research and making my own mistakes.