Although there is a strong demand, the utilization of automated segmentation approaches in histopathological imaging is often inhibited by a high degree of variability. To tackle the thereby arising challenges, we propose an unsupervised “gradual” domain adaptation framework which exploits the knowledge that disease progression is a gradual process and that the approximate level-of-progression is known. We extend an existing approach by adding two methods for regularization of the fully-unsupervised adaptation process. Experiments performed on three datasets corresponding to three different renal pathologies showed excellent segmentation accuracies. The framework is not restricted to the considered task, but can also be adapted to other similar (biomedical) application scenarios.