# Is active learning able to detect challenging cases?

Let's say we have a set of data points that need to be labelled for a classification task. In the pool-based active learning, if we go with the uncertainty measure, is the AL approach able to detect challenging cases? By challenging cases I mean samples that receive a high prediction score for $$\hat{y}$$ (e.g. >90%) but, most probably, $$\neg\hat{y}$$ is the correct prediction.

The rationale behind my question is: does adding more samples to the training set always improve the performance of a classifier?