  Deep learning has achieved widespread success in medical image analysis,
leading to an increasing demand for large-scale expert-annotated medical image
datasets. Yet, the high cost of annotating medical images severely hampers the
development of deep learning in this field. To reduce annotation costs, active
learning aims to select the most informative samples for annotation and train
high-performance models with as few labeled samples as possible. In this
survey, we review the core methods of active learning, including the evaluation
of informativeness and sampling strategy. For the first time, we provide a
detailed summary of the integration of active learning with other
label-efficient techniques, such as semi-supervised, self-supervised learning,
and so on. We also summarize active learning works that are specifically
tailored to medical image analysis. Additionally, we conduct a thorough
comparative analysis of the performance of different AL methods in medical
image analysis with experiments. In the end, we offer our perspectives on the
future trends and challenges of active learning and its applications in medical
image analysis.
