I'd add that there is a deep connection between active learning and understanding the "domain of expertise" of a model, for example what inputs are ambiguous or low confidence, and which are out of distribution. E.g. BALD is a form of out of distribution detection - a point with high disagreement it not only useful to add to the training pool, it is a point for which the current model has no business making a prediction.