This paper proposes an adaptive prediction time horizon based model predictive steering control algorithm for autonomous vehicle with disturbance estimation. The model predictive control (MPC) requires relatively accurate system model to compute the reasonable control inputs. There exists model uncertainty between system model and actual system. And the uncertainty can be increased in the prediction step of MPC and that can have an influence on control performance. In order to address the issue, the uncertainty has been estimated using the sliding mode observer and predicted in the designed prediction algorithm based on gray prediction model. Using the predicted future uncertainties, the number of prediction steps has been determined with the uncertainty threshold value. The path tracking control algorithm for autonomous driving has been used to compute the steering angle of vehicle. The performance evaluation of the adaptive MPC has been conducted using the simplified bicycle model in Matlab/Simulink environment under the lane change scenario.