This paper proposes a new scoring calibration method named ``Consistency-Aware Score Calibration", which introduces a Consistency Measure Factor (CMF) to measure the stability of audio voiceprints in similarity scores for speaker verification. The CMF is inspired by the limitations in segment scoring, where the segments with shorter length are not friendly to calculate the similarity score. By using CMF as a scale to calibrate scores calculated on the whole audio length, the method improves the performance of different state-of-the-art systems significantly, including ResNet with 34 to 518 layers and RepVGG. Experimental results show that the CMF method is better than segment scoring and shows excellent complementary information with other normalization or calibration methods. The proposed method was first proposed in a system description for the VoxCeleb speaker recognition challenge 2023, where it achieved the 1st place in Track1 of the challenge.