Feature Attribution Uncertainty is a methodological construct in model uncertainty visualization that quantifies the variability and reliability of feature importance estimates derived from interpretability techniques, distinguishing it from general model uncertainty by focusing specifically on the confidence associated with input features' contributions to model predictions.