Cheers. It's a good thing to be wary of. Poor use of active learning will end up biasing the data according to the model it's trained on – so that data won't be the best X samples to train on a different model. Most of this issue comes from bad active learning selection methods. If you have well calibrated uncertainty estimates and sample for diversity and representiveness too, it's far less of a concern.