Deep support vector data description (DeSVDD) is an emerging anomaly detection method based on the deep learning methodology. However, few studies take the confidence of DeSVDD predictions into account so that the present DeSVDD models cannot indicate the reliability degree of the anomaly detection results. In this paper, we enrich the theory of DeSVDD by building the model confidence definition and developing the corresponding calibration strategy. For one thing, by revisiting the methodology of DeSVDD-based anomaly detection, the confidence of detection results is presented to indicate if the prediction of DeSVDD is reliable. For another, we propose a modified power T-scaling strategy to smooth the anomaly scores of DeSVDD model and improve its calibration performance without changing the original detection results. Six open experiment datasets are used to perform the method testing and the experimental results confirm the effectiveness of our proposed calibration strategy.