  Density of mitotic figures in histologic sections is a prognostically
relevant characteristic for many tumours. Due to high inter-pathologist
variability, deep learning-based algorithms are a promising solution to improve
tumour prognostication. Pathologists are the gold standard for database
development, however, labelling errors may hamper development of accurate
algorithms. In the present work we evaluated the benefit of multi-expert
consensus (n = 3, 5, 7, 9, 11) on algorithmic performance. While training with
individual databases resulted in highly variable F$_1$ scores, performance was
notably increased and more consistent when using the consensus of three
annotators. Adding more annotators only resulted in minor improvements. We
conclude that databases by few pathologists and high label accuracy may be the
best compromise between high algorithmic performance and time investment.
