High-dimensional imaging, especially Imaging Mass Cytometry (IMC), has greatly enhanced analytical capabilities of cellular and tissue exploration. Cell segmentation, a crucial step for such analysis, has concomitantly improved with the development of deep learning tools, which typically require extensive annotated datasets to reach a high level of efficacy. In this thesis, we present a new cell segmentation tool based on a Bayesian Convolutional Neural Network capable of estimating output uncertainty. We also introduce an "offline" active learning framework leveraging uncertainty to enhance the efficiency of the annotation process, allowing for improved model training with fewer annotated images. The model and framework are tested on an independent IMC dataset, composed of multiple proprietary and public images, obtained from heterogeneous tissues.