We've found you can get an order of magnitude improvement in the amount of labelled data you need - but there is some variance based on the difficulty of the problem. Because you are retraining the model in tandem with the data labelling process, there is additional compute associated to an active learning powered labelling process versus just selecting the data at random to label next. But this additional compute consideration is almost always outweighed by the saving of human time spent labelling.