Batch-mode active learning: How to select the batch that will bring the largest model improvement
Your question is actually the whole point of active learning. You probably need to read about existing approaches in active learning in order to find the one which suits your needs.

I'm not up to date at all on the topic but a traditional approach was to train several models on available data, make them predict on all the unannotated instances then use majority voting: instances for which the models tend to agree are "easy" to predict, whereas those for which models make different predictions are "hard" so potentially more valuable to improve performance.