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Biomedical image analysis algorithm validation depends on high-qualityannotation of reference datasets, for which labeling instructions are key.Despite their importance, their optimization remains largely unexplored. Here,we present the first systematic study of labeling instructions and their impacton annotation quality in the field. Through comprehensive examination ofprofessional practice and international competitions registered at the MICCAISociety, we uncovered a discrepancy between annotators' needs for labelinginstructions and their current quality and availability. Based on an analysisof 14,040 images annotated by 156 annotators from four professional companiesand 708 Amazon Mechanical Turk (MTurk) crowdworkers using instructions withdifferent information density levels, we further found that including exemplaryimages significantly boosts annotation performance compared to text-onlydescriptions, while solely extending text descriptions does not. Finally,professional annotators constantly outperform MTurk crowdworkers. Our studyraises awareness for the need of quality standards in biomedical image analysislabeling instructions.

Labeling instructions matter in biomedical image analysis