While unlabelled data can be largely available and even abundant, the annotation process can be quite expensive and limiting. Under the assumption that some samples are more important for a given task than others, active learning targets the problem of identifying the most informative samples that one should acquire annotations for. In this work we propose a simple sample selection criterion that moves beyond the conventional reliance on model uncertainty as proxy to leverage new labels. By first accepting the model prediction and then judging its effect on the generalization error, we can better identify wrongly predicted samples. We also present a very efficient approximation to our criterion, providing a similarity-based interpretation. In addition to evaluating our method on the standard benchmarks of active learning, we consider the challenging yet realistic imbalanced data scenario. We show state-of-the-art results, especially on the imbalanced setting, and achieve better rates at identifying wrongly predicted samples than existing active learning methods. Our method is simple, model agnostic and relies on the current model status without the need for re-training from scratch.
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