Radiotherapy has been shown to increase risks of cardiotoxicities for breast cancer patients. Automated delineation approaches are necessary for consistent and efficient assessment of cardiac doses in large, retrospective datasets, while patient-specific estimation of the uncertainty in these doses provides valuable additional data for modelling and understanding risks. In this work, we aim to validate the consistency of our previously described open-source software model for automatic cardiac delineation in the context of dose assessment, relative to manual contouring. We also extend our software to introduce a novel method to automatically quantify the uncertainty in cardiac doses based on expected inter-observer variability (IOV) in contouring. This method was applied to a cohort of 15 left-sided breast cancer patients treated in Denmark using modern tangential radiotherapy techniques. On each image set, the whole heart and left anterior descending coronary artery (LADCA) were contoured by nine independent experts; the range of doses to these nine volumes provided a reference for the dose uncertainties generated from the automatic method. Local and external atlas sets were used to test the method. Results give confidence in the consistency of automatic segmentations, with mean whole heart dose differences for local and external atlas sets of −0.20 ± 0.17 and −0.10 ± 0.14 Gy, respectively. Automatic estimates of uncertainties in doses are similar to those from IOV for both the whole heart and LADCA. Overall, this study confirms that our automated approach can be used to accurately assess cardiac doses, and the proposed method can provide a useful tool in estimating dose uncertainties.