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The performance of learning-based algorithms improves with the amount oflabelled data used for training. Yet, manually annotating data is particularlydifficult for medical image segmentation tasks because of the limited expertavailability and intensive manual effort required. To reduce manual labelling,active learning (AL) targets the most informative samples from the unlabelledset to annotate and add to the labelled training set. On the one hand, mostactive learning works have focused on the classification or limitedsegmentation of natural images, despite active learning being highly desirablein the difficult task of medical image segmentation. On the other hand,uncertainty-based AL approaches notoriously offer sub-optimal batch-querystrategies, while diversity-based methods tend to be computationally expensive.Over and above methodological hurdles, random sampling has proven an extremelydifficult baseline to outperform when varying learning and sampling conditions.This work aims to take advantage of the diversity and speed offered by randomsampling to improve the selection of uncertainty-based AL methods forsegmenting medical images. More specifically, we propose to compute uncertaintyat the level of batches instead of samples through an original use ofstochastic batches (SB) during sampling in AL. Stochastic batch querying is asimple and effective add-on that can be used on top of any uncertainty-basedmetric. Extensive experiments on two medical image segmentation datasets showthat our strategy consistently improves conventional uncertainty-based samplingmethods. Our method can hence act as a strong baseline for medical imagesegmentation. The code is available on:https://github.com/Minimel/StochasticBatchAL.git.

Active learning for medical image segmentation with stochastic batches