This paper concerns how machine learning explainability advances understanding of self-supervised learning for ultrasound video. We define the explainability as capturing anatomy-aware knowledge and propose a new set of quantitative metrics to evaluate explainability. We validate our proposed explainability approach on medical fetal ultrasound video self-supervised learning and demonstrate how it can guide the choice of self-supervised learning method. Our approach is attractive as it reveals biologically meaningful patterns which may instil human (clinician) trust in the trained model.