Estimation error covariance is a measure of the uncertainty in the estimated state of a system, quantifying how much the estimated values deviate from the true state due to noise and model inaccuracies. It plays a crucial role in filtering and prediction algorithms, such as the Kalman filter, by providing a metric to update and refine state estimates over time.