AI-assisted nuclei segmentation in histopathological images is a crucial task in the diagnosis and treatment of cancer diseases and it decreases the time required to manually screen microscopic tissue images. However, lack of labeled data is a significant barrier for deep learning-based approaches. In this study, we propose a novel approach to nuclei segmentation that leverages the available labelled and unlabelled data by combining the strengths of both transductive and inductive learning. To the best of our knowledge, this is the first study to propose such a hybrid approach for medical image segmentation. Moreover, we propose a novel two-stage transductive inference scheme. We evaluate our approach on MoNuSeg benchmark to demonstrate the efficacy and potential of our method.