What is the term for the learning setting in which the algorithm itself chooses which unlabeled examples to have labeled, typically the most uncertain or informative ones, to reach high accuracy with fewer labels?

Active learning

Active learning reduces labeling cost by querying an oracle only for the most valuable points, using strategies like uncertainty sampling or query-by-committee. It is most useful when unlabeled data is abundant but labels are expensive to obtain.