Motivation: Backbone structures and solvent accessible surface area of proteins are benefited from continuous real value prediction because it removes the arbitrariness of defining boundary between different secondary-structure and solvent-accessibility states.However,lacking the confidence score for predicted values has limited their applications.Here we investigated whether or not we can make a reasonable prediction of absolute errors for predicted backbone torsion angles,Cα-atom-based angles and torsion angles,solvent accessibility,contact numbers and half-sphere exposures by employing deep neural networks.Results: We found that angle-based errors can be predicted most accurately with Spearman correlation coefficient (SPC) between predicted and actual errors at about 0.6.This is followed by sol-vent accessibility (SPC~0.5).The errors on contact-based structural properties are most difficult to predict (SPC between 0.2 and 0.3).We showed that predicted errors are significantly better error indicators than the average errors based on secondary-structure and amino-acid residue types.Such error or confidence indictors are expected to be useful for protein structure prediction and refinement.Availability: The method is available at http://sparks-lab.org as a part of SPIDER2 package.