Self-motion is an essential but often overlooked component of sound localisation. While the directional information of a source is implicitly contained in head-centred acoustic cues, that acoustic input needs to be continuously combined with sensorimotor information about the head orientation in order to decode these cues to a world-centred frame of reference. On top of that, the use of head movement significantly reduces ambiguities in the directional information provided by the incoming sound. In this work, we evaluate a Bayesian model that predicts dynamic sound localisation, by comparing its predictions to human performance measured in a behavioural sound-localisation experiment. Model parameters were set a-priori, based on results from various psychoacoustic and sensorimotor studies, i.e., without any post-hoc parameter fitting to behavioral results. In a spatial analysis, we evaluated the model's capability to predict spatial localisation responses. Further, we investigated specific effects of the stimulus duration, the spatial prior and sizes of various model uncertainties on the predictions. The spatial analysis revealed general agreement between the predictions and the actual behaviour. The altering of the model uncertainties and stimulus duration revealed a number of interesting effects providing new insights on modelling the human integration of acoustic and sensorimotor information in a localisation task.