A few neural networks in biological image segmentation rely on a prediction of a distance map. This principle is at the basis of popular software such as Stardist, Cellpose or Omnipose. It yields unprecedented accuracy but hinges on fully annotated datasets. This can be a serious limitation for generating training sets and performing transfer learning. In this paper, we show how to handle partial annotation, while still relying on the distance map. We design a variant of the Omnipose architecture embedded in a user-friendly Napari plugin. We evaluate the performance of the proposed approach in the contexts of frugal learning, transfer learning and regular learning on a large database. Our experiments show that the proposed approach can lead to substantial savings in time and resources without sacrificing segmentation quality.