Single point active alignment method is a widely used calibration method for optical-see-through head-mounted displays (OST-HMDs) since its appearance. It always requires high-accuracy alignment for data acquisition, and the collected data affect the calibration accuracy to a large extent. However, there are often many kinds of alignment errors occurring in the calibration process. These errors may contain random errors of manual alignment and system errors of the fixed eye-HMD model. To tackle these problems, we first leverage a random sample consensus approach to recurrently decrease the random error of the collected data sequence and use a region-induced data enhancement method to reduce the system error. We design a typical framework to enhance the data acquisition for calibration, sequentially reducing the random error and the system error. Experimental results show that the proposed method can significantly make the calibration more robust due to the elimination of sampling points with large errors. At the same time, the calibration accuracy can be increased by the proposed dynamic eye-HMD model that takes the eye movement into consideration. The improvement about calibration should be significant to promote the applications based on OST-HMDs.