Venue: MIDL 2023 Short_Paper_Track

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: This paper aims to address limitations of active learning for medical image segmentation. Here, the authors propose to incorporate randomness in uncertainty-based sampling, thereby improving the diversity of the labeled samples. 

-	The approach seems reasonable, direct, scalable
-	Performance gains are shown when the proposed scheme is used in conjunction with 4 uncertainty-based measures, and across varying hyperparameters and initial labeled sets. 

review_title: flexible, effective approach

final_score: rating: 7: Good paper, accept
review_content: The authors propose an active learning strategy for medical image segmentation (a field that is still relatively unexplored in the deep learning era) based on computing uncertaintty at the batch level - in order to mitigate the problems associated with potentially selecting outliers, when operating at the sample level. Using a prostate dataset, the authors show that the method (which is very easy to implement, said as a good thing) produces modest but consistent (and statistically significant) improvements across a number of metrics and uncertainty criteria. The paper is well written and is very clear (goes straight to the point). 

PS: a (very minor) slap on the wrist to the authors, for withholding their names & institutions on the pdf, which saves space (maybe they were not aware that short papers were single-blind - in which case, I still want to slap them on the wrist, but this time for not reading the instructions!)
review_title: simple (and/but) good results

Decision: Accept(Poster)