Significance Pulse oximetry estimates the arterial oxygen saturation of hemoglobin (SaO2) based on relative changes in light intensity at the cardiac frequency. Commercial pulse oximeters require empirical calibration on healthy volunteers, resulting in limited accuracy at low oxygen levels. An accurate, self-calibrated method for estimating SaO2 is needed to improve patient monitoring and diagnosis. Aim Given the challenges of calibration at low SaO2 levels, we pursued the creation of a self-calibrated algorithm that can effectively estimate SaO2 across its full range. Our primary objective was to design and validate our calibration-free method using data collected from human subjects. Approach We developed an algorithm based on diffuse optical spectroscopy measurements of cardiac pulses and the modified Beer–Lambert law (mBLL). Recognizing that the photon mean pathlength ( ⟨ L ⟩ ) varies with SaO2 related absorption changes, our algorithm aligns/fits the normalized ⟨ L ⟩ (across wavelengths) obtained from optical measurements with its analytical representation. We tested the algorithm with human freedivers performing breath-hold dives. A continuous-wave near-infrared spectroscopy probe was attached to their foreheads, and an arterial cannula was inserted in the radial artery to collect arterial blood samples at different stages of the dive. These samples provided ground-truth SaO2 via a blood gas analyzer, enabling us to evaluate the accuracy of SaO2 estimation derived from the NIRS measurement using our self-calibrated algorithm. Results The self-calibrated algorithm significantly outperformed the conventional method (mBLL with a constant ⟨ L ⟩ ratio) for SaO2 estimation through the diving period. Analyzing 23 ground-truth SaO2 data points ranging from 41% to 100%, the average absolute difference between the estimated SaO2 and the ground truth SaO2 is 4.23 % ± 5.16 % for our algorithm, significantly lower than the 11.25 % ± 13.74 % observed with the conventional approach. Conclusions By factoring in the variations in the spectral shape of ⟨ L ⟩ relative to SaO2, our self-calibrated algorithm enables accurate SaO2 estimation, even in subjects with low SaO2 levels.