Deep learning models are effective for medical image analysis tasks such as segmentation. However, training these models requires substantial amounts of labeled data, most often annotated manually. Segmenting new medical images to create labeled training data is a tedious and time-consuming process for human annotators. Interactive segmentation tools seek to alleviate this problem, most often by predicting completed segmentations from limited user inputs. This works reasonably well for some domains and for well-defined tasks. But for a new domain or task, the segmentation task is ambiguous. We hypothesize than in such situations proposing multiple partial segmentations is more useful than proposing a single complete segmentation. We propose a probabilistic partial segmentation model, that takes an input image and partial segmentation, and predicts possible next steps for the segmentation. The proposed model can be used iteratively to help annotators accurately and efficiently segment new medical images. The user can choose among multiple predicted larger segmentations and perhaps make a small number of corrections before inputting the updated segmentation back into the system. By predicting multiple larger partial segmentations at each iteration rather than attempting to fully complete the segmentation in one step, the system can enable users to produce accurate segmentations for new medical image domains with fewer corrections. We use synthetic data to demonstrate the proposed model and show a proof-of-concept for the system.