Generally, Shannon defined information entropy is used to measure information uncertainty, whereas, Onicescu defined information energy is used to measure information certainty. However, information energy and Shannon entropy display a dual relationship. Furthermore, the cumulative residual entropy is employed to estimate the information uncertainty by replacing the probability distribution function of Shannon entropy with the cumulative distribution function. Based on this, a new method to measure information certainty—cumulative residual information energy—is proposed and applied to image threshold segmentation. To overcome the shortcomings of complex calculation and the low efficiency of accumulated residual information energy, a recursive algorithm is used here to increase the running speed of image threshold segmentation. Our experimental results show that the proposed method outperforms the classical maximum entropy threshold method and other related threshold segmentation methods used for natural images and cell blood smear images.