  Since labeling medical image data is a costly and labor-intensive process,
active learning has gained much popularity in the medical image segmentation
domain in recent years. A variety of active learning strategies have been
proposed in the literature, but their effectiveness is highly dependent on the
dataset and training scenario. To facilitate the comparison of existing
strategies and provide a baseline for evaluating novel strategies, we evaluate
the performance of several well-known active learning strategies on three
datasets from the Medical Segmentation Decathlon. Additionally, we consider a
strided sampling strategy specifically tailored to 3D image data. We
demonstrate that both random and strided sampling act as strong baselines and
discuss the advantages and disadvantages of the studied methods. To allow other
researchers to compare their work to our results, we provide an open-source
framework for benchmarking active learning strategies on a variety of medical
segmentation datasets.
