With the growing density of IoT (Internet of Things) devices for environment and process monitoring, there is an increasing need for optimal spectrum sharing between the IoT devices. The goal is to efficiently schedule transmissions from IoT devices in such a way that the target processes can be tracked as accurately as possible. Towards this, we propose an error-based scheduling policy in two different settings: statistics of the target processes are known and unknown. We characterize the performance of the proposed error based policy both analytically and through simulations for Markovian target processes like birth-death chains and Gaussian processes. We compare the proposed scheduling policy with the age of information based policies like MAF (Maximum Age First), using MSE (mean square error) as the performance metric. We demonstrate better performance of our policy in settings where the parameters of the processes are known and unknown. A broad class of AoI-based wireless scheduling policies, such as the MAF policy, Maximum Weighted policy, Whittle Index policy, etc, which have recently shown promise in preserving the freshness of information, can be seen as special cases of our proposed policies.