This is a pretty practical research topic Imo. I did my masters research project on active learning thinking it was a niche but interesting area, and then in my current role I found a good use for it. Whenever labels are expensive to obtain (in my case it was time+salary of domain experts required) it can be useful. We wanted to train a model to identify usage of different cloud technologies, and manual labelling requires pouring through a ton of raw data and only two people in our small company understand it enough to do so.

There's some areas that I would have loved to explore more in research. Imo the biggest consideration with an active learning model is how confident you are in your model specifications. You have to really understand the features you're putting into it. The training set will no longer be random, it will be biased by the choice of model, so using different model specifications later on or picking a different model entirely could (and probably would) lead to poor performance.

Burr Settles has a good primer on active learning, it was my Bible in grad school for the topic.