Data is the lifeblood of machine learning. It is crucial for practitioners to maximize the accuracy and diversity of data supplied to a model during training in order to achieve a reliable and generalizable final product. The target a model is trained to predict determines what data can be used. For instance, a model designed to produce segmentations may be trained to generate one-hot encoded or signed boundary distance predictions [Heinrich et al., 2021]. A network trained to predict the presence of a given structure in terms of a binary mask can be accurately trained on datasets indicating the objects presence, as well as data sparsely representing its absence (see section 2.1). However, this is not the case for signed boundary distance predictions (see section 2.2). As a result, datasets labeling the presence of structures mutually exclusive to the target objects, but not the targets themselves, cannot be used to train a signed boundary distance prediction model. We introduce hot-distance to incorporate the benefits of signed boundary distance prediction while maintaining the ability to train models on a larger body of datasets. An empirical study of this strategy's effects, compared to existing methods (i.e.