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Deep learning has achieved widespread success in medical image analysis,leading to an increasing demand for large-scale expert-annotated medical imagedatasets. Yet, the high cost of annotating medical images severely hampers thedevelopment of deep learning in this field. To reduce annotation costs, activelearning aims to select the most informative samples for annotation and trainhigh-performance models with as few labeled samples as possible. In thissurvey, we review the core methods of active learning, including the evaluationof informativeness and sampling strategy. For the first time, we provide adetailed summary of the integration of active learning with otherlabel-efficient techniques, such as semi-supervised, self-supervised learning,and so on. Additionally, we also highlight active learning works that arespecifically tailored to medical image analysis. In the end, we offer ourperspectives on the future trends and challenges of active learning and itsapplications in medical image analysis.

A comprehensive survey on deep active learning and its applications in medical image analysis