I'm not sure the algorithm would emit any answer in the case where everyone has an equal level of objective foundation for their subjective belief.
But it would probably help in cases where popular opinion is entirely misinformed about the subjective question, not having any basis other than (already misinformed) hearsay on which to form their own subjective opinion.
So, for example, if there was a musician who had an absolutely terrible song that somehow became the song they were best known for (being a "one-hit wonder" whose song wasn't really a "hit"), the public might believe that that song is their best song, since it's the only song of theirs the public has ever heard of. Experts (i.e. people who have heard more than the one song of theirs), on the other hand, would tend to agree that it's certainly not their best song.
(Given that example, I'm inclined to suggest that you could use this algorithm to determine when people are being judged overly-harshly for things, e.g. whether to ban someone from a website just because they've received a lot of reports about that person's behavior.)
The example uses it to spot a case where most people are wrong, but some large minority of people expect that most people will answer incorrectly, while themselves answering correctly. A large enough difference (10%, in the example case) between the "what do you guess others will answer?" and what people actually answered indicates the majority opinion is, in fact, wrong.
But it would probably help in cases where popular opinion is entirely misinformed about the subjective question, not having any basis other than (already misinformed) hearsay on which to form their own subjective opinion.
So, for example, if there was a musician who had an absolutely terrible song that somehow became the song they were best known for (being a "one-hit wonder" whose song wasn't really a "hit"), the public might believe that that song is their best song, since it's the only song of theirs the public has ever heard of. Experts (i.e. people who have heard more than the one song of theirs), on the other hand, would tend to agree that it's certainly not their best song.
(Given that example, I'm inclined to suggest that you could use this algorithm to determine when people are being judged overly-harshly for things, e.g. whether to ban someone from a website just because they've received a lot of reports about that person's behavior.)