Poor survival for breast cancer in low- and middle-income countries is largely attributed to late-stage diagnosis and limited access to diagnostic tools. Therefore, we propose using point-of-care ultrasound (POCUS) as a low-cost diagnostic solution paired with a deep learning (DL) model for cancer detection. While using DL has shown great potential, it is crucial to ensure the trustworthiness of a model for diagnostic support in a setting with minimally trained healthcare workers, as a wrong prediction can lead to severe consequences. In the present study, we investigated different measures of trustworthiness from the field of uncertainty quantification, including deep ensembles, Bayesian neural networks and softmax score, for the application of breast cancer detection in POCUS imaging. The results show that all methods exhibit a correlation between uncertainty scores and correctness of prediction. The correlation was strongest when using an average ensemble and an entropy-based total predictive uncertainty. When excluding 30% of the test samples based on highest uncertainty scores, the area under the receiver operating characteristic curve (AUC) for cancer detection increases significantly from 95.6% to 98.9% (with 95% confidence intervals [93.3, 97.0] to [97.5, 99.9]), comparable to an expert radiologist’s performance.